A STAC Extension for discovering and cataloguing 3D city models

National-scale 3D city model datasets are growing rapidly—Japan’s PLATEAU project alone publishes over 170 000 files, and the Netherlands’ 3D BAG covers all 10+ million buildings in the country—yet no standardised mechanism exists for discovering and cataloguing these datasets across repositories and data infrastructures. Essential properties such as coordinate reference systems, levels of detail, and city object types remain embedded inside data files in diverse formats (e.g. CityGML, CityJSON, or FlatCityBuf), invisible to search engines and catalogue services. This paper presents three contributions to address this gap. First, we define the STAC 3D City Models Extension, a formal extension to the SpatioTemporal Asset Catalog (STAC) specification that adds metadata fields for levels of detail, city object types, semantic surfaces, textures, materials, and attribute schemas. Second, we develop city3dstac, an open-source command-line tool written in Rust that automatically extracts metadata from 3D city models in different formats and generates standards-conformant STAC metadata. Third, we construct a prototype registry that currently contains 53 STAC Collections, of which 31 are fully indexed, totalling 14 766 STAC Items. The indexed Collections include the national-scale 3D BAG, American Cities, Estonia, and PLATEAU datasets. Existing STAC clients can be used for basic browsing of the catalogue without modification, while extension-aware clients can support richer 3D-specific filtering.

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Publication Details

Journal
ISPRS annals of the photogrammetry, remote sensing and spatial information sciences
Published
2026-09-28
DOI
https://doi.org/10.5194/isprs-annals-xii-4-w1-2026-25-2026
Primary Topic
3D Modeling in Geospatial Applications
Type
article
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article

A STAC Extension for discovering and cataloguing 3D city models

Hugo Ledoux, Hidemichi Baba
ISPRS annals of the photogrammetry, remote sensing and spatial information sciences
3D Modeling in Geospatial Applications
article

A STAC Extension for discovering and cataloguing 3D city models

Hugo Ledoux, Hidemichi Baba
article en

Abstract

National-scale 3D city model datasets are growing rapidly—Japan’s PLATEAU project alone publishes over 170 000 files, and the Netherlands’ 3D BAG covers all 10+ million buildings in the country—yet no standardised mechanism exists for discovering and cataloguing these datasets across repositories and data infrastructures. Essential properties such as coordinate reference systems, levels of detail, and city object types remain embedded inside data files in diverse formats (e.g. CityGML, CityJSON, or FlatCityBuf), invisible to search engines and catalogue services. This paper presents three contributions to address this gap. First, we define the STAC 3D City Models Extension, a formal extension to the SpatioTemporal Asset Catalog (STAC) specification that adds metadata fields for levels of detail, city object types, semantic surfaces, textures, materials, and attribute schemas. Second, we develop city3dstac, an open-source command-line tool written in Rust that automatically extracts metadata from 3D city models in different formats and generates standards-conformant STAC metadata. Third, we construct a prototype registry that currently contains 53 STAC Collections, of which 31 are fully indexed, totalling 14 766 STAC Items. The indexed Collections include the national-scale 3D BAG, American Cities, Estonia, and PLATEAU datasets. Existing STAC clients can be used for basic browsing of the catalogue without modification, while extension-aware clients can support richer 3D-specific filtering.

ISPRS annals of the photogrammetry, remote sensing and spatial information sciencesVol. XII-4/W1-2026(0)
Delft University of Technology (NL)
Industry, innovation and infrastructure
Openalex Percentile: Top 15%
3D Modeling in Geospatial Applications
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A STAC Extension for discovering and cataloguing 3D city models — Hugo Ledoux, Hidemichi Baba · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences (2026) | TGRS Research Map | TGRS